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第六届美国航空航天学会计算流体动力学阻力预测研讨会结果的统计分析

Statistical Analysis of Solutions from the Sixth AIAA Computational Fluid Dynamics Drag Prediction Workshop.

作者信息

Derlaga Joseph M, Morrison Joseph H

机构信息

NASA Langley Research Center, Hampton, Virginia, 23681.

出版信息

J Aircr. 2019 Jul;55(4):1388-1400. doi: 10.2514/1.C034938. Epub 2019 Jul 19.

Abstract

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from North America, Europe, Asia, and South America using both common and custom grid sequences as well as multiple turbulence models for the June 2016 6 AIAA CFD Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was the Common Research Model subsonic transport wing-body previously used for both the 4 and 5 Drag Prediction Workshops. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with previous workshops. Most notably, the results reinforce a lesson learned from the 5 Drag Prediction Workshop regarding the importance of a common grid sequence in decreasing solution variation and demonstrate that predicted drag increments show less scatter between codes than the predicted absolute values of drag for a given configuration.

摘要

一个图形框架被用于对雷诺平均纳维-斯托克斯计算流体动力学代码集合进行广泛的N版本测试结果的统计分析。这些解决方案由来自北美、欧洲、亚洲和南美洲的代码开发者和用户获得,他们在2016年6月由美国航空航天学会应用空气动力学技术委员会主办的第六届美国航空航天学会计算流体力学阻力预测研讨会上,使用了通用和自定义网格序列以及多种湍流模型。本次研讨会的空气动力学构型是之前用于第四届和第五届阻力预测研讨会的通用研究模型亚音速运输机翼身。这项工作延续了早期研讨会上开始的统计分析,并将最新研讨会的网格收敛研究结果与之前的研讨会进行比较。最值得注意的是,这些结果强化了从第五届阻力预测研讨会中学到的关于通用网格序列在减少解的变化方面的重要性的经验教训,并表明对于给定构型,预测的阻力增量在代码之间的离散度比预测的阻力绝对值要小。

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